1. Technical Field
The present invention relates generally to processing locomotive biometrics, and in particular to determining, quantifying, and classifying gait patterns. More particularly, the present invention relates to a method, apparatus, system, and article of manufacture for detecting and processing gait pattern data in a manner enabling correlation with neuromuscular conditions associated therewith.
2. Description of the Related Art
Auto-locomotion in humans and animals is a complex neuromuscular activity. The dynamic characteristics of such locomotion are often referred to as gait. Temporal and distance factors of gait are often referred to as stride characteristics. Given the dependence of gait characteristics on a multitude of neuromuscular factors, the study of gait, particularly the variability of stride and/or other gait-related metrics, may be very useful for medical diagnostic and prognostic purposes.
Conventional systems and techniques for analyzing gait include clinician observation, kinematics methods, and kinetic methods. Extensive training and experience are required for a clinician to accurately assess subject's gait pattern and correlate such observations with underlying neuromuscular conditions. Even with extensive training and experience, clinician observation of gait is considerably imprecise, particularly for evaluating subtle gait characteristics that may be useful in detecting slight changes in gait useful for diagnostic and prognostic purposes.
Kinematics methods are an objective alternative, and are generally designed to measure linear and angular motion of various body parts during gait cycles using specialized targeted video camera technology. Kinetic methods employ various devices such as accelerometers, force platforms, etc., for determining the magnitude and timing of forces acting on various body parts during gait cycles. Implementing any one of the aforementioned gait analysis methods is a very complex and expensive undertaking, due in part to the complexity of the detection devices and the multifaceted signal detection and processing steps required.
An alternative method for assessing gait characteristics directly measures muscle function and is known as dynamic electromyography (EMG). EMG analysis utilizes a myoelectric signal that parallels the intensity of corresponding muscle activity, thus providing a useful indicator of the resulting mechanical effect. Determining and distinguishing the alterations in magnitude, phase, and duration of muscle action as associated with a particular gait pattern is very complex and computationally intensive.
An alternative type of gait detection device is a portable “footswitch” apparatus that is wearable on or within footwear, thus enabling collection of gait cycle data in a simpler, less expensive, and less restrictive manner than the previously described techniques. One such device is described in U.S. Pat. No. 6,360,597, issued to Hubbard. The gait analysis system disclosed by Hubbard comprises a force-detecting shoe insert enabling collection of gait interval data as the subject freely walks. While footswitch type devices, such as the portable gait analyzer disclosed by Hubbard, overcomes some of the aforementioned problems relating to the collection of gait pattern data, problems associated with the processing and analysis of collected gait data remain unresolved.
Since a wide variety of neuromuscular impairments manifest themselves to at least some extent in terms gait stability, an important analytic function is the determination of gait rhythmic patterns in terms of stride-to-stride fluctuations. Several methods are currently utilized to temporally analyze several dynamic aspects of gait. Power spectral analysis, such as Fourier spectral analysis has been a standard for temporal analysis of the dynamics of stride interval data presented as a time series. Autocorrelation decay is another such temporal spectral analysis technique often utilized in a complementary manner in conjunction with Fourier spectral analysis. The autocorrelation function estimates the extent to which a time series is self-correlated over different time lags, thus providing a measure of the system's “memory.”
Processing the limited data points provided by a footswitch system using the foregoing gait analysis techniques is inadequate for detecting subtle alterations in gait patterns, and furthermore provides no means for associating a specified gait pattern with an underlying neuromuscular status. It can therefore be appreciated that a need exists for a system and method that efficiently collects gait data, quantifies the stability of gait from the collected data, and classifies the collected gait data in association with a neuromuscular status. The present invention addresses these as well as other needs unaddressed by prior art gait analysis devices and systems.